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Detecting Potential Design Weaknesses in SHADE Through Network Feature Analysis

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Book cover Hybrid Artificial Intelligent Systems (HAIS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10334))

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Abstract

This preliminary study presents a hybridization of two research fields – evolutionary algorithms and complex networks. A network is created by the dynamic of an evolutionary algorithm, namely Success-History based Adaptive Differential Evolution (SHADE). Network feature, node degree centrality, is used afterward to detect potential design weaknesses of SHADE algorithm. This approach is experimentally tested on the CEC2015 benchmark set of test functions and future directions in the research are proposed.

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Acknowledgements

This work was supported by Grant Agency of the Czech Republic – GACR P103/15/06700S, further by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme Project no. LO1303 (MSMT-7778/2014). Also by the European Regional Development Fund under the Project CEBIA-Tech no. CZ.1.05/2.1.00/03.0089 and by Internal Grant Agency of Tomas Bata University under the Projects no. IGA/CebiaTech/2017/004.

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Authors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlin, T.G. Masaryka 5555, 760 01, Zlin, Czech Republic

    Adam Viktorin, Michal Pluhacek, Roman Senkerik & Tomas Kadavy

Authors
  1. Adam Viktorin
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  2. Michal Pluhacek
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  3. Roman Senkerik
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  4. Tomas Kadavy
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Corresponding author

Correspondence to Tomas Kadavy .

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Editors and Affiliations

  1. University of La Rioja , Logroño, La Rioja, Spain

    Francisco Javier Martínez de Pisón

  2. University of La Rioja , Logroño, La Rioja, Spain

    Rubén Urraca

  3. University of A Coruña , Ferrol, La Coruña, Spain

    Héctor Quintián

  4. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Viktorin, A., Pluhacek, M., Senkerik, R., Kadavy, T. (2017). Detecting Potential Design Weaknesses in SHADE Through Network Feature Analysis. In: Martínez de Pisón, F., Urraca, R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2017. Lecture Notes in Computer Science(), vol 10334. Springer, Cham. https://doi.org/10.1007/978-3-319-59650-1_56

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  • DOI: https://doi.org/10.1007/978-3-319-59650-1_56

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59649-5

  • Online ISBN: 978-3-319-59650-1

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